🚀 JobMatch AI – Career Recommendation System

A simple end-to-end demo for job title recommendations using classical ML. Train a RandomForestClassifier on text features, then serve predictions via a Streamlit app.

✨ Features

• Training: Build model from tabular/text data (company, skills, description)
• Serving: Interactive UI with Streamlit
• Artifacts: Saves job_recommendation_model.pkl, vectorizer.pkl, label_encoder.pkl

🗂️ Project Structure

app/
  app1.py           # Streamlit UI
model/
  al.py            # Training script (creates .pkl artifacts)
data/
  raw_data/
    fake_news_dataset.csv  # Sample placeholder (not used by current training script)
README.md

📦 Requirements

See requirements.txt. Install with:

pip install -r requirements.txt

Contents:

pandas
numpy
matplotlib
seaborn
scikit-learn
joblib
streamlit

🧰 Setup

1. Ensure Python 3.9+ is installed.
2. Create and activate a virtual environment (recommended):

python -m venv .venv
# Windows PowerShell
. .venv\Scripts\Activate.ps1

3. Install dependencies:

pip install -r requirements.txt

📝 Data Notes

• The training script model/al.py currently reads a dataset from an absolute path:
  • C:\Users\indur\Downloads\job_recommendation_dataset.csv
• Update this path inside model/al.py to point to your dataset, or place your CSV in the repo and use a relative path (e.g., data/raw_data/job_recommendation_dataset.csv).
• Required columns expected by the script:
  • Job Title, Company, Skills Required, Description.

🏋️ Train the Model

Run the training script to produce the model artifacts:

python model/al.py

This will save:

• job_recommendation_model.pkl
• vectorizer.pkl
• label_encoder.pkl

🖥️ Run the App

Start the Streamlit app:

streamlit run app/app1.py

Then open the URL shown in the terminal (usually http://localhost:8501).

🧪 Usage

• Enter optional Company, comma-separated Skills, and an optional Description.
• Click "Recommend Job" to see the predicted job title.

⚠️ Tips & Troubleshooting

• If you see file-not-found errors for .pkl files, run the training step first.
• If you see a CSV path error, fix the dataset path in model/al.py.
• For best results, ensure your dataset contains the expected columns and encoding (UTF-8).

📜 License

This project is for educational/demo purposes. Add a license if you plan to distribute.

🙌 Acknowledgements

• Built with pandas, scikit-learn, and streamlit.